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Record W4211098183 · doi:10.1016/j.ejrh.2022.101031

Changes to the hydrology of a boreal fen following the placement of an access road and below ground pipeline

2022· article· en· W4211098183 on OpenAlexafffundabout
Matthew C. Elmes, Richard M. Petrone, Olena Volik, Jonathan S. Price

Bibliographic record

VenueJournal of Hydrology Regional Studies · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
FundersAlberta Environment and ParksSuncor Energy Incorporated
KeywordsHydrology (agriculture)ClearancePeatCulvertMireWater tableFlow (mathematics)Environmental sciencePipeline transportGeologyGroundwaterGeotechnical engineeringGeographyGeometryEnvironmental engineeringMathematics

Abstract

fetched live from OpenAlex

A channel fen in the Athabasca Oil Sands Area, Alberta, Canada We assessed the hydrological changes to the hydrology of a moderate-rich fen after the construction of a road (perpendicular to flow) in 2003 and a pipeline (obliquely to flow) in 2011. Flow obstruction was most prominent where the fen intersected the road. Changes to hydrophysical properties from pipeline construction were most pronounced in the top 10 cm of peat, which demonstrated significantly higher bulk density (by 170% and 112%) and lower hydraulic conductivity (by 94% and 91%) above the buried pipeline and in adjacent cleared locations, respectively, relative to areas not cleared or directly disturbed during pipeline development. Changes to water table levels from the pipeline were more pronounced farther down-gradient as the pipeline cut through the fen obliquely to direction of flow, and water tables became more variable on the side where the flow face had decreased in length. If built through peatlands, pipelines should be oriented parallel to flow direction and located along the central axis of the fen. Additional culverts should be considered in the event of building a pipeline through an already existing road to facilitate flow on either side.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.300
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2022
Admission routes3
Has abstractyes

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